Waveform tomography in 2.5D: Parameterization for crooked‐line acquisition geometry
Bibliographic record
Abstract
Abstract A method for 2.5D viscoacoustic waveform tomography that can be applied to generate 2D models of velocity and attenuation from inversion of refraction waveforms on land seismic reflection data acquired along crooked roads is developed. It is particularly useful for typical crustal reflection surveys. First‐arrival travel time tomography is applied using a 3D method, but with constraints on the intermediate 3D velocity model; the result is the starting model for the next step. A 2.5D frequency‐domain full‐waveform inversion stage parameterizes 3D geometry in the seismic source and receiver arrays, with the assumption that the velocity and attenuation models are homogeneous in the out‐of‐plane direction. This approach results in superior results compared to a strictly 2D approach when the acquisition line is crooked, with a moderate increase in computational cost. A case study using data acquired in the Nechako Basin in south‐central British Columbia, Canada, exemplifies and validates the procedure. The velocity model derived from 2.5D waveform tomography is compared with that from a previous study in which 2D waveform tomography was applied to the same data set and with results from 3D travel time tomography. The resolution and accuracy of the velocity model from 2.5D waveform tomography are demonstrated to be greater than those from travel time or 2D waveform tomography. A model of viscoacoustic attenuation, which was not possible in the 2D case, is also generated. These models are interpreted jointly to highlight features of geological interest, such as a sedimentary basin, basement rocks, and faults, from surface to about 3 km depth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".